mlsanityData to 7 Oct 2026

Efficient EUD Parsing

arXiv 2006.00838 ↗First posted 1 Jun 2020cs.CLcited by 8

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Abstract (arXiv)

We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020. We engaged with the task by focusing on efficiency. For this we considered training costs and inference efficiency. Our models are a combination of distilled neural dependency parsers and a rule-based system that projects UD trees into EUD graphs. We obtained an average ELAS of 74.04 for our official submission, ranking 4th overall.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.

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Citing papers per year: 2021: 7; 2022: 1.

8 papers.

  1. 2021A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021cited by 9
  2. 2021Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsingcited by 7
  3. 2021Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performancecited by 5
  4. 2021Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modelingcited by 4
  5. 2021TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuningcited by 3
  6. 2021Splitting EUD graphs into trees: A quick and clatty approachcited by 1
  7. 2021The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Taskcited by 1
  8. 2022The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performancecited by 0